CASIA OpenIR
Progressive rectification network for irregular text recognition
Gao, Yunze1,2; Chen, Yingying1; Wang, Jinqiao1; Lu, Hanqing1
Source PublicationSCIENCE CHINA-INFORMATION SCIENCES
ISSN1674-733X
2020-01-14
Volume63Issue:2Pages:14
Corresponding AuthorChen, Yingying(yingying.chen@nlpr.ia.ac.cn)
AbstractScene text recognition has received increasing attention in the research community. Text in the wild often possesses irregular arrangements, which typically include perspective, curved, and oriented texts. Most of the existing methods do not work well for irregular text, especially for severely distorted text. In this paper, we propose a novel progressive rectification network (PRN) for irregular scene text recognition. Our PRN progressively rectifies the irregular text to a front-horizontal view and further boosts the recognition performance. The distortions are removed step by step by leveraging the observation that the intermediate rectified result provides good guidance for subsequent higher quality rectification. Additionally, by decomposing the rectification process into multiple procedures, the difficulty of each step is considerably mitigated. First, we specifically perform a rough rectification, and then adopt iterative refinement to gradually achieve optimal rectification. Additionally, to avoid the boundary damage problem in direct iterations, we design an envelope-refinement structure to maintain the integrity of the text during the iterative process. Instead of the rectified images, the text line envelope is tracked and continually refined, which implicitly models the transformation information. Then, the original input image is consistently utilized for transformation based on the refined envelope. In this manner, the original character information is preserved until the final transformation. These designs lead to optimal rectification to boost the performance of succeeding recognition. Extensive experiments on eight challenging datasets demonstrate the superiority of our method, especially on irregular benchmarks.
Keywordirregular text recognition progressive rectification iterative refinement
DOI10.1007/s11432-019-2710-7
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61806200] ; National Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61806200]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000514581400001
PublisherSCIENCE PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38377
Collection中国科学院自动化研究所
Corresponding AuthorChen, Yingying
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Gao, Yunze,Chen, Yingying,Wang, Jinqiao,et al. Progressive rectification network for irregular text recognition[J]. SCIENCE CHINA-INFORMATION SCIENCES,2020,63(2):14.
APA Gao, Yunze,Chen, Yingying,Wang, Jinqiao,&Lu, Hanqing.(2020).Progressive rectification network for irregular text recognition.SCIENCE CHINA-INFORMATION SCIENCES,63(2),14.
MLA Gao, Yunze,et al."Progressive rectification network for irregular text recognition".SCIENCE CHINA-INFORMATION SCIENCES 63.2(2020):14.
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